Updating README and pictures, and removing unused functions

Author: gusamarante

README.md

@@ -1,5 +1,5 @@
 [paper_website]: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr340.pdf
-[inference_atribute]: https://github.com/gusamarante/pyacm/blob/ba641c14e450fc83d22db4ef5e60eadbd489b351/pyacm/acm.py#L203
+
 
 # pyacm
 Implementation of ["Pricing the Term Structure with Linear Regressions" from 
@@ -18,14 +18,14 @@ carries all the relevant variables as atributes:
 - Term premium
 - Historical in-sample expected returns 
 - Expected return loadings
-- Hypothesis testing (Not sure if correct, more info observations below)
 
 
 # Instalation
 ```bash
 pip install pyacm
 ```
 
+
 # Usage
 ```python
 from pyacm import NominalACM
@@ -42,13 +42,9 @@ The tricky part of using this model is getting the correct data format. The
 - Maturities (columns) must be equally spaced in **monthly** frequency and start 
 at month 1. This means that you need to construct a bootstraped curve for every 
 date and interpolate it at fixed monthly maturities
-- Whichever maturity you want to be the longest, your input data should have one
-column more. For example, if you want term premium estimate up to the 10-year 
-yield (120 months), your input data should include maturities up to 121 months. 
-This is needed to properly compute the returns.
 
-# Examples
 
+# Examples
 The estimates for the US are available on the [NY FED website](https://www.newyorkfed.org/research/data_indicators/term-premia-tabs#/overview).
 
 The jupyter notebook [`example_br`](https://github.com/gusamarante/pyacm/blob/main/example_br.ipynb) 
@@ -65,14 +61,5 @@ contains an example application to the Brazilian DI futures curve that showcases
 > FRB of New York Staff Report No. 340, 
 > Available at SSRN: https://ssrn.com/abstract=1362586 or http://dx.doi.org/10.2139/ssrn.1362586
 
-The version of the article that was published by the NY FED is not 100% explicit on how the data is being manipulated, 
-but I found an earlier version of the paper on SSRN where the authors go deeper into the details on how everything is being estimated:
-- Data for zero yields uses monthly maturities starting from month 1
-- All principal components and model parameters are estiamted with data resampled to a monthly frequency, averaging observations in each month
-- To get daily / real-time estimates, the factor loadings estimated from the monthly frquency are used to transform the daily data
-
-
-# Observations
-I am not completely sure that computations in the [inferences attributes][inference_atribute] 
-are correct. If you find any mistakes, please open a pull request following the contributing 
-guidelines.
+I would like to thank Emanuel Moench for sending me his original MATLAB code in 
+order to perfectly replicate these results.

images/DI observed vs risk neutral.png

@@ -27,7 +27,6 @@
         'pandas',
         'scikit-learn',
         'statsmodels',
-        'tqdm',
     ],
     keywords=[
         'asset pricing',